This Is Not My Populous State

With the release of the 2020 US Census’ topline data, we can see which state populations increased and which few decreased. And in that we can sort, or resort, states by population. The Washington Post did this a few weeks ago with an interactive ranking chart in a nice online article. (I’d be curious what the print version was, alas I only receive the New York Times.)

The piece begins with a nice intro motion graphic that selects states and shows how their ranking among the other states (plus the District of Columbia, DC), has evolved since 1920.

Here we see the fall of Iowa in the rankings.

After scrolling down briefly, the reader enters a portion of the story displayed by keeping the hero graphic static whilst blurbs of texts scroll over the lines. As the blurbs move past, different states or sets of states become highlighted to draw attention to them.

I guess I’m picking on Iowa?

This works really well. When discussing the case of Iowa vis-a-vis the growth of California, Texas, and Florida, I don’t need to see the story of Nebraska. Especially as the end of the piece features this hero graphic as an interactive, explore-the-data piece of content. I don’t have a screenshot of that, because it’s really just the above two but with a dropdown selector and a legend.

As the user scrolls through the story, they move past the semi-motion graphic and into a text-driven narrative for each region of the United States. I’ve highlighted only the Northeast, where I was born, raised, and presently live. As an aside, I remember my family completing the 2000 Census around the kitchen table. The 2010 Census I filled out at a small desk not long after I moved into my second flat in Chicago. And this most recent one I completed whilst under quarantine here in Philadelphia.

The Northeast. Definitely not Iowa.

This section of the article uses static images with the region’s constituent states highlighted. Again, this works really well, because when looking at the Northeast, I’m still not interested in Nebraska. And also again, we have the interactive explorer at the end of the article.

Overall this is a really strong piece from the Post. I have some quibbles with the design, primarily I don’t understand the function of the connecting lines’ fades and curves. But I find neither too terribly distracting from the content of the graphic.

Credit for the piece goes to Harry Stevens and Nick Kirkpatrick.

2020 Census Apportionment

Every ten years the United States conducts a census of the entire population living within the United States. My genealogy self uses the federal census as the backbone of my research. But that’s not what it’s really there for. No, it exists to count the people to apportion representation at the federal level (among other reasons).

The founding fathers did not intend for the United States to be a true democracy. They feared the tyranny of mob rule as majority populations are capable of doing and so each level of the government served as a check on the other. The census-counted people elected their representatives for the House, but their senators were chosen by their respective state legislatures. But I digress, because this post is about a piece in the New York Times examining the new census apportionment results.

I received my copy of the Times two Tuesdays ago, so these are photos of the print piece instead of the digital, online editions. The paper landed at my front door with a nice cartogram above the fold.

A cartogram exploded.

Each state consists of squares, each representing one congressional district. This is the first place where I have an issue with the graphic, admittedly a minor one. First we need to look at the graphic’s header, “States That Will Gain or Los Seats in the Next Congress” and then look at the graphic. It’s unclear to me if the squares therefore represent the states today with their numbers of districts, or if we are looking at a reapportioned map. Up in Montana, I know that we are moving from one at-large seat to two seat, and so I can resolve that this is the new apportionment. But I am left wondering if a quick phrase or sentence that declares these represent the 2022 election apportionment and not those of this past decade would be clearer?

Or if you want a graphic treatment, you could have kept all the states grey, but used an unfilled square in those states, like Pennsylvania and Illinois, losing seats, and then a filled square in the states adding seats.

Inside the paper, the article continued and we had a few more graphics. The above graphic served as the foundation for a second graphic that charted the changing number of seats since 1910, when the number of seats was fixed.

Timeline of gains and losses

I really like this graphic. My issue here is more with my mobile that took the picture. Some of these states appear quite light, and they are on the printed page. However, they are not quite as light as these photos make them out to be. That said, could they be darker? Probably. Even in print, the dark grey “no change” instances jump out instead of perhaps falling to the background.

The remaining few graphics are far more straightforward, one isn’t even a graphic technically.

First we have two maps.

Good old primary colours.

Nothing particularly remarkable here. The colours make a lot of sense, with red representing Republicans and blue Democrats. Yellow represents independent commissions and grey is only one state, Pennsylvania, where the legislature is controlled by Republicans and the governorship by Democrats.

Finally we have a table with the raw numbers.

Tables are great for organising information. Do you have a state you’re most curious about, Illinois for example? If so, you can quickly scan down the state column to find the row and then over to the column of interest. What tables don’t allow you to do is quickly identify any visual patterns. Here the designers chose to shade the cells based on positive/negative changes, but that’s not highlighting a pattern.

Overall, this was a really strong piece from the Times. With just a few language tweaks on the front page, this would be superb.

Credit for the piece goes to Weyi Cai and the New York Times graphics department.

Philly Falls from Fifth

Well it finally happened. While the Great Recession spared Philadelphia for several years, Phoenix has finally moved up into the rank of fifth-largest city in the United States.

There are some notable differences that this graphic captures. The big one is that Philly is relatively small at 135 square miles. Phoenix is half the size of Rhode Island. What the graphic does not capture, however, is that Philly is still growing, albeit more slowly than southern and western cities. Because also in the news is the fact that Chicago has shrunk and lost people. Personally I count as a -1 for Chicago and a +1 for Philly.

Comparing size and population
Comparing size and population

Credit for the piece goes to the graphics department.

What It Means to be Black in the US…Census

As I said yesterday, I’m up in northern Wisconsin. But sometime later today I should be starting a long drive back to Chicago. So let me continue with one more piece of genealogy- and information-related content that is especially relevant given recent events. Vox posted an article a couple of days ago that looked at the definition of black via census options. Of particular interest is the supplemental  or sidebar information: whether you could choose your own race or whether it was chosen for you by the enumerator.

A history of choices
A history of choices

Maybe it’s only a coincidence that the 1890 census records went up in flames.

Credit for the piece goes to the Vox graphics department.

White (Immigrant) People

This is an old map that saw the light of day a while back. Featured on Vox, the map supports the notion that some white people are whiter than other white people. The map explores immigrant populations. Using a map for spatial arrangement of integrated components, the data looks at immigrants’ ethnic origins, their workforce breakdown, and their recent growth.

A look at PA, my ancestors are in that data set
A look at PA, my ancestors are in that data set

Credit for the piece goes to FS Howell. (I presume.)